Recent Trends in Artificial Intelligence and Data Sciences : Select Proceedings of the 15th International Conference — CONFLUENCE 2025 (Lecture Notes in Electrical Engineering)

Kumar, Sumit (EDT)/ Aggrawal, Garima (EDT)/ Unhelkar, Bhuvan (EDT)

Springer Nature Switzerland AG（2025/09発売）

• 製本 Hardcover:ハードカバー版／ページ数 500 p.
• 言語 ENG
• 商品コード 9789819692026

Full Description

This book presents original, peer-reviewed research papers from the International Conference on Recent Trends in Artificial Intelligence and Data Sciences—CONFLUENCE 2025. It highlights the latest advancements across diverse areas of data science and computational techniques. The volume focuses on artificial intelligence, machine learning, deep learning, soft computing, and other emerging methodologies. By sharing recent findings in these domains, the book serves as a valuable resource for researchers, scientists, industry professionals, and students alike.

Contents

A Comparative Analysis of Existing and AI-Driven Frameworks for Industrial Interaction Practices.- A Comprehensive Review: Machine and Deep Learning Techniques for Sentiment Analysis on Datasets of Political Tweets.- Comparative Analysis of Feature Selection Techniques for Malware Detection in URLs.- Google and Flower Federated Learning Frameworks Comparison in Bug Prediction in terms of Flexibility and Technical Factors.- Exploring Sentiments in Stack Overflow Score and Discussion: A Dual Approach with Machine Learning Models and Expert Evaluation.- An AI-Enhanced Framework for Mental Health Management.- dvancing Diabetic Retinopathy Detection through Collaborative Vision Transformer and CNN Architectures Integrated with Explainable AI.- Diagnosis and Prediction of Multiple Sclerosis Disease Using Quantum Machine Learning Classifiers.- Predictive Modeling for Breast Cancer Diagnosis Using Machine Learning Algorithms.- Early Detection of Cardiovascular Disease through the Utilization of Multiple Machine Learning Techniques.- Early Prediction and Detection of Liver Disease Using Deep Learning.- Predictive Analytics based on Public Health Response using Epidemiological Models: A Data Driven Approach.- Prognosis, and Diagnosis of Diabetes through Minkowski Distance Metric.- Mental Health Reaction Based On Behavioral Analysis And Digital Device Usage Using Machine Learning Algortihm.- Automated Onset Seizure Detection Using EEG Signals by Machine Learning.